Abstract
In the past decades, the electrical power system is designed and developed to satisfy the owner demand that continuously appears in many variations. Hence, engineers have put their full effort to solve the problem associated with electrical power systems that come and might arise in the future. Therefore, distributed generation (DG) has been introduced to solve multiple electrical power system problems. The proposed methodology presented in this study focuses on minimizing network power losses, improving the voltage profile of system operation, and security constraints in a distribution. It is known that the location and capacity of DG play significant roles in the system losses in a distribution system. A hybrid metaheuristic nature-inspired algorithm is presented in this study for optimal location and sizing of multiple DG units. The best location and optimal sizing of DGs will be determined through Hybrid metaheuristic of Artificial Immune System Firefly Algorithm (AISFA). The designated technique will be tested into IEEE-69 test system using MATLAB software. For reducing the power losses, the simulation results have shown that bus 61 is the best location for reducing power losses and improving voltage profile in IEEE-69 test system in the preliminary result. By installing DG at bus 61, the real power losses improve about 89%, with a voltage profile improvement index up to 1.249099.
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Acknowledgements
The authors would like to acknowledge the support from the Fundamental Research Grant Scheme (FRGS) under a grant number of FRGS/1/2019/TK07/UNIMAP/02/9 from the Ministry of Education (MOE) Malaysia and Universiti Malaysia Perlis.
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Husni, N.N.H.M., Rahim, S.R.A., Adzman, M.R. (2022). A Hybrid Optimization Approach for Power Loss Reduction and Voltage Profile Improvement in Distribution System. In: Md. Zain, Z., Sulaiman, M.H., Mohamed, A.I., Bakar, M.S., Ramli, M.S. (eds) Proceedings of the 6th International Conference on Electrical, Control and Computer Engineering. Lecture Notes in Electrical Engineering, vol 842. Springer, Singapore. https://doi.org/10.1007/978-981-16-8690-0_20
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DOI: https://doi.org/10.1007/978-981-16-8690-0_20
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